efficientnet_b0_scratch_improvement.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"51aa0773"},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import os"],"id":"51aa0773"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":47014,"status":"ok","timestamp":1645248664199,"user":{"displayName":"Nafiur Rahman Khadem","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgJW9Ph_91dtNKoctY_b9UrSdwntYNcKvR0aI17wQ=s64","userId":"14304020629346378249"},"user_tz":-360},"id":"b4372804","outputId":"aff462dd-260d-4b6d-f3db-f858eaadcc2d"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"id":"b4372804"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1645248664201,"user":{"displayName":"Nafiur Rahman Khadem","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgJW9Ph_91dtNKoctY_b9UrSdwntYNcKvR0aI17wQ=s64","userId":"14304020629346378249"},"user_tz":-360},"id":"61350420","outputId":"bc7831c0-c1dc-4409-ae3d-70fdccaf2f53"},"outputs":[{"name":"stdout","output_type":"stream","text":["/content/drive/.shortcut-targets-by-id/11vZDVRncVAa-HSQzhEWDgaoMu2b1KfSR/ML Project\n","/content/drive/.shortcut-targets-by-id/11vZDVRncVAa-HSQzhEWDgaoMu2b1KfSR/ML Project\n"]}],"source":["%cd '/content/drive/MyDrive/ML Project'\n","!pwd"],"id":"61350420"},{"cell_type":"code","execution_count":null,"metadata":{"id":"53e749a9"},"outputs":[],"source":["import tensorflow as tf\n","from sklearn.metrics import classification_report\n","from tensorflow.keras.utils import image_dataset_from_directory"],"id":"53e749a9"},{"cell_type":"code","execution_count":null,"metadata":{"id":"adb27fbb"},"outputs":[],"source":["import matplotlib.pyplot as plt\n","\n","\n","def plot_metric(hist, metric):\n","    plt.plot(hist.history[f\"{metric}\"], label=f\"{metric}\")\n","    plt.plot(hist.history[f\"val_{metric}\"], label=f\"val_{metric}\")\n","    plt.title(f\"{metric} Function Evolution\")\n","    plt.legend()\n","\n","\n","def plot_hist(hist):\n","    plt.figure(figsize=(12, 4))\n","    plt.subplot(1, 2, 1)\n","    plot_metric(hist, 'loss')\n","\n","    plt.subplot(1, 2, 2)\n","    plot_metric(hist, 'accuracy')"],"id":"adb27fbb"},{"cell_type":"code","execution_count":null,"metadata":{"id":"7336ed0f"},"outputs":[],"source":["butterfly_path = 'butterfly-images40-species/'"],"id":"7336ed0f"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14418,"status":"ok","timestamp":1645248681974,"user":{"displayName":"Nafiur Rahman Khadem","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgJW9Ph_91dtNKoctY_b9UrSdwntYNcKvR0aI17wQ=s64","userId":"14304020629346378249"},"user_tz":-360},"id":"eea16f38","outputId":"95f91b5c-b6d1-4ca2-e197-a7a86f7475b8"},"outputs":[{"name":"stdout","output_type":"stream","text":["Found 9295 files belonging to 75 classes.\n"]}],"source":["train_ds = image_dataset_from_directory(butterfly_path + 'train')\n"],"id":"eea16f38"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":651,"status":"ok","timestamp":1645248682620,"user":{"displayName":"Nafiur Rahman Khadem","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgJW9Ph_91dtNKoctY_b9UrSdwntYNcKvR0aI17wQ=s64","userId":"14304020629346378249"},"user_tz":-360},"id":"2dc5f393","outputId":"7b3da801-dacd-4c72-aa2e-7ff899f11ff2"},"outputs":[{"name":"stdout","output_type":"stream","text":["Found 375 files belonging to 75 classes.\n"]}],"source":["validation_ds = image_dataset_from_directory(butterfly_path + 'valid')\n"],"id":"2dc5f393"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5544,"status":"ok","timestamp":1645248688161,"user":{"displayName":"Nafiur Rahman Khadem","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgJW9Ph_91dtNKoctY_b9UrSdwntYNcKvR0aI17wQ=s64","userId":"14304020629346378249"},"user_tz":-360},"id":"0b0f1677","outputId":"ac38c69c-5fb4-427f-f42c-33e896914a79"},"outputs":[{"name":"stdout","output_type":"stream","text":["Found 375 files belonging to 75 classes.\n"]}],"source":["test_ds = image_dataset_from_directory(\n","    butterfly_path + 'test',\n","    shuffle=False)  # when shuffle is True, it auto shuffles on each iteration\n"],"id":"0b0f1677"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1645248688162,"user":{"displayName":"Nafiur Rahman Khadem","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgJW9Ph_91dtNKoctY_b9UrSdwntYNcKvR0aI17wQ=s64","userId":"14304020629346378249"},"user_tz":-360},"id":"d2299e82","outputId":"9a3aef19-b5df-4c68-c8aa-b41044969194"},"outputs":[{"name":"stdout","output_type":"stream","text":["['ADONIS', 'AFRICAN GIANT SWALLOWTAIL', 'AMERICAN SNOOT', 'AN 88', 'APPOLLO', 'ATALA', 'BANDED ORANGE HELICONIAN', 'BANDED PEACOCK', 'BECKERS WHITE', 'BLACK HAIRSTREAK', 'BLUE MORPHO', 'BLUE SPOTTED CROW', 'BROWN SIPROETA', 'CABBAGE WHITE', 'CAIRNS BIRDWING', 'CHECQUERED SKIPPER', 'CHESTNUT', 'CLEOPATRA', 'CLODIUS PARNASSIAN', 'CLOUDED SULPHUR', 'COMMON BANDED AWL', 'COMMON WOOD-NYMPH', 'COPPER TAIL', 'CRECENT', 'CRIMSON PATCH', 'DANAID EGGFLY', 'EASTERN COMA', 'EASTERN DAPPLE WHITE', 'EASTERN PINE ELFIN', 'ELBOWED PIERROT', 'GOLD BANDED', 'GREAT EGGFLY', 'GREAT JAY', 'GREEN CELLED CATTLEHEART', 'GREY HAIRSTREAK', 'INDRA SWALLOW', 'IPHICLUS SISTER', 'JULIA', 'LARGE MARBLE', 'MALACHITE', 'MANGROVE SKIPPER', 'MESTRA', 'METALMARK', 'MILBERTS TORTOISESHELL', 'MONARCH', 'MOURNING CLOAK', 'ORANGE OAKLEAF', 'ORANGE TIP', 'ORCHARD SWALLOW', 'PAINTED LADY', 'PAPER KITE', 'PEACOCK', 'PINE WHITE', 'PIPEVINE SWALLOW', 'POPINJAY', 'PURPLE HAIRSTREAK', 'PURPLISH COPPER', 'QUESTION MARK', 'RED ADMIRAL', 'RED CRACKER', 'RED POSTMAN', 'RED SPOTTED PURPLE', 'SCARCE SWALLOW', 'SILVER SPOT SKIPPER', 'SLEEPY ORANGE', 'SOOTYWING', 'SOUTHERN DOGFACE', 'STRAITED QUEEN', 'TROPICAL LEAFWING', 'TWO BARRED FLASHER', 'ULYSES', 'VICEROY', 'WOOD SATYR', 'YELLOW SWALLOW TAIL', 'ZEBRA LONG WING']\n"]}],"source":["class_names = train_ds.class_names\n","class_count = len(class_names)\n","print(class_names)"],"id":"d2299e82"},{"cell_type":"code","execution_count":null,"metadata":{"id":"049e52fa"},"outputs":[],"source":["# inputs must match the same form the model was originally trained on\n","IMG_SIZE = 224\n","size = (IMG_SIZE, IMG_SIZE)\n","train_ds = train_ds.map(lambda image, label:\n","                        (tf.image.resize(image, size), label))\n","validation_ds = validation_ds.map(lambda image, label:\n","                                  (tf.image.resize(image, size), label))\n","test_ds = test_ds.map(lambda image, label:\n","                      (tf.image.resize(image, size), label))\n"],"id":"049e52fa"},{"cell_type":"code","execution_count":null,"metadata":{"id":"cc82c2ca"},"outputs":[],"source":["img_augmentation = tf.keras.models.Sequential(\n","    [\n","        tf.keras.layers.RandomRotation(factor=0.15),\n","        tf.keras.layers.RandomTranslation(height_factor=0.1, width_factor=0.1),\n","        tf.keras.layers.RandomFlip(),\n","        tf.keras.layers.RandomContrast(factor=0.1),\n","    ],\n","    name=\"img_augmentation\",\n",")"],"id":"cc82c2ca"},{"cell_type":"code","execution_count":null,"metadata":{"id":"342bdbf5"},"outputs":[],"source":["model = tf.keras.models.load_model('efficient_b0_30_epochs_scratch.h5')"],"id":"342bdbf5"},{"cell_type":"markdown","metadata":{"id":"6Mu2Vmm9eCx3"},"source":["## Try smaller learning rate"],"id":"6Mu2Vmm9eCx3"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":581},"id":"9AOHGMaKeA2o","outputId":"b0fbb012-528e-419a-d843-20f3e641f33c","executionInfo":{"status":"error","timestamp":1645260625828,"user_tz":-360,"elapsed":1166387,"user":{"displayName":"Nafiur Rahman Khadem","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgJW9Ph_91dtNKoctY_b9UrSdwntYNcKvR0aI17wQ=s64","userId":"14304020629346378249"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","291/291 [==============================] - 2386s 8s/step - loss: 0.3994 - accuracy: 0.8936 - val_loss: 0.9747 - val_accuracy: 0.7947\n","Epoch 2/10\n","291/291 [==============================] - 2309s 8s/step - loss: 0.3710 - accuracy: 0.9012 - val_loss: 0.9612 - val_accuracy: 0.8080\n","Epoch 3/10\n","291/291 [==============================] - 2323s 8s/step - loss: 0.3348 - accuracy: 0.9125 - val_loss: 0.9430 - val_accuracy: 0.8187\n","Epoch 4/10\n","291/291 [==============================] - 2312s 8s/step - loss: 0.3259 - accuracy: 0.9159 - val_loss: 0.9368 - val_accuracy: 0.8213\n","Epoch 5/10\n","291/291 [==============================] - 2303s 8s/step - loss: 0.3078 - accuracy: 0.9187 - val_loss: 0.9134 - val_accuracy: 0.8240\n"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-15-3ad5e8d6cfe4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m               \u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sparse_categorical_crossentropy'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m               metrics=['accuracy'])\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mhist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_ds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_ds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mplot_hist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1371\u001b[0m       \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1372\u001b[0m       \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterator\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menumerate_epochs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1373\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1374\u001b[0m         \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_epoch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1375\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcatch_stop_iteration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["model.compile(tf.keras.optimizers.Adam(learning_rate=.00001),\n","              loss='sparse_categorical_crossentropy',\n","              metrics=['accuracy'])\n","hist = model.fit(train_ds, validation_data=validation_ds, epochs=10)\n","plot_hist(hist)"],"id":"9AOHGMaKeA2o"},{"cell_type":"code","source":["model.save('efficient_b0_35_epochs_scratch.h5')"],"metadata":{"id":"HiUmJ69XHQ6S"},"id":"HiUmJ69XHQ6S","execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21568,"status":"ok","timestamp":1645260647379,"user":{"displayName":"Nafiur Rahman Khadem","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgJW9Ph_91dtNKoctY_b9UrSdwntYNcKvR0aI17wQ=s64","userId":"14304020629346378249"},"user_tz":-360},"id":"d38c7e2e","outputId":"e2ab05c8-a457-46d2-b0ff-a69ca7c85874"},"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.83      1.00      0.91         5\n","           1       1.00      1.00      1.00         5\n","           2       1.00      0.60      0.75         5\n","           3       1.00      0.80      0.89         5\n","           4       0.71      1.00      0.83         5\n","           5       1.00      0.80      0.89         5\n","           6       1.00      1.00      1.00         5\n","           7       1.00      1.00      1.00         5\n","           8       0.33      0.40      0.36         5\n","           9       0.83      1.00      0.91         5\n","          10       0.67      0.80      0.73         5\n","          11       1.00      0.80      0.89         5\n","          12       1.00      0.80      0.89         5\n","          13       0.83      1.00      0.91         5\n","          14       0.80      0.80      0.80         5\n","          15       0.83      1.00      0.91         5\n","          16       1.00      0.60      0.75         5\n","          17       0.80      0.80      0.80         5\n","          18       1.00      0.60      0.75         5\n","          19       0.50      0.20      0.29         5\n","          20       0.67      0.80      0.73         5\n","          21       0.50      0.60      0.55         5\n","          22       0.80      0.80      0.80         5\n","          23       0.75      0.60      0.67         5\n","          24       1.00      1.00      1.00         5\n","          25       1.00      1.00      1.00         5\n","          26       0.71      1.00      0.83         5\n","          27       0.00      0.00      0.00         5\n","          28       1.00      1.00      1.00         5\n","          29       1.00      0.80      0.89         5\n","          30       0.67      0.80      0.73         5\n","          31       0.50      0.40      0.44         5\n","          32       1.00      1.00      1.00         5\n","          33       0.71      1.00      0.83         5\n","          34       1.00      1.00      1.00         5\n","          35       1.00      1.00      1.00         5\n","          36       1.00      0.60      0.75         5\n","          37       0.80      0.80      0.80         5\n","          38       0.25      0.20      0.22         5\n","          39       0.71      1.00      0.83         5\n","          40       1.00      0.60      0.75         5\n","          41       0.80      0.80      0.80         5\n","          42       1.00      0.80      0.89         5\n","          43       1.00      0.80      0.89         5\n","          44       0.50      0.60      0.55         5\n","          45       0.71      1.00      0.83         5\n","          46       0.75      0.60      0.67         5\n","          47       0.56      1.00      0.71         5\n","          48       1.00      0.80      0.89         5\n","          49       0.83      1.00      0.91         5\n","          50       0.67      0.40      0.50         5\n","          51       1.00      1.00      1.00         5\n","          52       1.00      0.80      0.89         5\n","          53       1.00      0.80      0.89         5\n","          54       1.00      0.80      0.89         5\n","          55       1.00      0.80      0.89         5\n","          56       0.67      0.80      0.73         5\n","          57       1.00      0.60      0.75         5\n","          58       1.00      1.00      1.00         5\n","          59       0.36      0.80      0.50         5\n","          60       1.00      0.60      0.75         5\n","          61       0.80      0.80      0.80         5\n","          62       0.80      0.80      0.80         5\n","          63       0.83      1.00      0.91         5\n","          64       0.67      0.80      0.73         5\n","          65       0.71      1.00      0.83         5\n","          66       0.62      1.00      0.77         5\n","          67       1.00      0.20      0.33         5\n","          68       1.00      0.20      0.33         5\n","          69       1.00      1.00      1.00         5\n","          70       0.50      0.80      0.62         5\n","          71       0.71      1.00      0.83         5\n","          72       0.71      1.00      0.83         5\n","          73       0.83      1.00      0.91         5\n","          74       1.00      0.80      0.89         5\n","\n","    accuracy                           0.79       375\n","   macro avg       0.82      0.79      0.78       375\n","weighted avg       0.82      0.79      0.78       375\n","\n"]}],"source":["y_pred = np.argmax(model.predict(test_ds), axis=-1)\n","y_test = np.concatenate(\n","    [label for batch, label in test_ds.as_numpy_iterator()])\n","print(classification_report(y_test, y_pred))"],"id":"d38c7e2e"}],"metadata":{"colab":{"name":"efficientnet_b0_scratch_improvement.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.12"},"papermill":{"default_parameters":{},"duration":10139.049871,"end_time":"2022-02-19T01:21:56.792896","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2022-02-18T22:32:57.743025","version":"2.3.3"}},"nbformat":4,"nbformat_minor":5}